If starter batteries, for example, are used as energy storage means in safety-critical consumers, then these energy storage means must be provided with status diagnoses in order to obtain information about whether the energy storage means is still in a position, due to its status, to supply the safety-critical consumer(s) with power or not.
In prior embodiments of a battery status detection, attempts were made to obtain reliable information about the charge status of an energy storage means, for example an automotive battery, through the use of heuristic methods such as evaluation of the off-load voltage and current integration. However, it is difficult to capture a complex and nonlinear system such as a starter battery using heuristic methods; a residual uncertainty remains which is intolerable in safety-critical systems. Heuristic methods inevitably involve an uncertainty since these methods cannot come close to including and taking into account all of the influences acting on a complex system. Current integration methods are encumbered with uncertainties because they can involve an incalculable accumulation of measurement errors and as a result no longer fulfill safety criteria that must be met by an energy storage means supplying power to a safety-critical consumer.
EP 0 505 333 A2 has disclosed a model-supported method for status detection and status prediction of an energy storage means. A model-supported status detection method has not previously been used, for example in automotive applications, because the model was too imprecise and the prior estimation algorithms were not sufficiently reliable and comprehensive.
The method proposed according to the invention permits a continuous detection and recognition of actual status values, i.e. of the charge status of an energy storage means. The model-supported battery status detection based on a filter, for example a Kalman filter, and a parameter estimator with on-line parameter identification that has a monitoring level and a predictor, permits a high-precision, very reliable energy storage means diagnosis for safety-critical consumers. The parameter estimation makes it possible to establish model parameters, which change due to defects or due to the aging of the energy storage means, and to continuously adapt the model on which the estimations are essentially based to the actual status and to track this status. The adapted model parameters are transferred to the filter, which is likewise designed based on a model whose estimation values can thus likewise be improved, as a result of which the prediction precision as a whole can be considerably increased. The division of the estimation of status values and parametersxe2x80x94on the one hand by the filter and on the other hand by the parameter estimatorsxe2x80x94results in the fact that incorrect estimates are prevented or become improbable since the model parameters being entered into the estimation routines and the actual status values of the energy storage means to be queried are always current. Incorrect estimates frequently occur when very large numbers of values have to be simultaneously estimated by the Kalman filter, as in the complex system of an energy storing means.
The parameters can be estimated in the parameter estimator in a different chronological horizon compared to the filter values, which means a reduction of the numerical complexity and therefore an easing of the burden on the processor. The estimation of the parameters by the parameter estimator, however, occurs frequently enough to promptly detect a sudden failure of the energy storage means.
A monitoring level can be used to test the plausibility of the values estimated by the parameter estimator and the filter. The monitoring level can activate the parameter estimator at any time. In terms of hierarchy, the monitoring level has precedence over both estimation routines, the parameter estimator and the Kalman filter. If the estimate values fall outside predefined ranges, the reset function, for example of the filter, can be initiated by the monitoring level.
The estimated values can be supplied to a predictor which, based on the actual status of the energy storage means, extrapolates its status with regard to the load of the safety-critical application. This permits the case, which is to be presumed unfavorable, to be simulated and tested as to whether, when the energy storage means is in a bad state, there is a risk of a possible and imminent failure of the energy storage means.